Julia package for the xtensor-julia library, the Julia bindings for xtensor.
-
xtensoris a C++ library for multi-dimensional arrays enabling numpy-style broadcasting and lazy computing. -
xtensor-juliaenables inplace use of julia arrays in C++ with all the benefits fromxtensor- C++ universal function and broadcasting
- STL - compliant APIs.
- A broad coverage of numpy APIs (see the numpy to xtensor cheat sheet).
The Julia bindings for xtensor are based on the CxxWrap.jl C++ library.
using Pkg; Pkg.add("Xtensor");
To get started with using Xtensor.jl and xtensor-julia, check out the full documentation
http://xtensor-julia.readthedocs.io/
xtensor-julia offers two container types wrapping julia arrays inplace to provide an xtensor semantics
jltensorjlarray.
Both containers enable the numpy-style APIs of xtensor (see the numpy to xtensor cheat sheet).
-
On the one hand,
jlarrayhas a dynamic number of dimensions. It can be reshaped dynamically and the new shape is reflected on the Julia side. -
On the other hand
jltensorhas a compile time number of dimensions, specified with a template parameter. Shapes ofjltensorinstances are stack allocated, makingjltensora significantly faster expression thanjlarray.
C++ code
#include <numeric> // Standard library import for std::accumulate #include <cxx_wrap.hpp> // libcxxwrap import to define Julia bindings #include "xtensor-julia/jltensor.hpp" // Import the jltensor container definition #include "xtensor/xmath.hpp" // xtensor import for the C++ universal functions double sum_of_sines(xt::jltensor<double, 2> m) { auto sines = xt::sin(m); // sines does not actually hold values. return std::accumulate(sines.cbegin(), sines.cend(), 0.0); } JLCXX_MODULE define_julia_module(jlcxx::Module& mod) { mod.method("sum_of_sines", sum_of_sines); }
Julia Code
using xtensor_julia_test arr = [[1.0 2.0] [3.0 4.0]] s = sum_of_sines(arr) s
Outputs
1.1350859243855171
C++ code
#include <cxx_wrap.hpp> #include "xtensor-julia/jlvectorize.hpp" double scalar_func(double i, double j) { return std::sin(i) - std::cos(j); } JLCXX_MODULE define_julia_module(jlcxx::Module& mod) { mod.method("vectorized_func", xt::jlvectorize(scalar_func)); }
Julia Code
using xtensor_julia_test x = [[ 0.0 1.0 2.0 3.0 4.0] [ 5.0 6.0 7.0 8.0 9.0] [10.0 11.0 12.0 13.0 14.0]] y = [1.0, 2.0, 3.0, 4.0, 5.0] z = vectorized_func(x, y) z
Outputs
[[-0.540302 1.257618 1.89929 0.794764 -1.040465],
[-1.499227 0.136731 1.646979 1.643002 0.128456],
[-1.084323 -0.583843 0.45342 1.073811 0.706945]]
xtensor-julia's documentation is built with three tools
While doxygen must be installed separately, you can install breathe by typing
pip install breathe
Breathe can also be installed with mamba (or conda)
mamba install -c conda-forge breathe
Finally, build the documentation with
make html
from the docs subdirectory.
Xtensor.jl depends on xtensor-julia, xtensor and CxxWrap libraries
Xtensor.jl |
xtensor |
xtensor-julia |
CxxWrap |
|---|---|---|---|
| master | >=0.24.2,<0.25 | 0.10.2 | >=0.12.0,<0.13 |
| 0.9.1 | >=0.24.2,<0.25 | 0.10.2 | >=0.12.0,<0.13 |
| 0.9.0 | >=0.24.0,<0.25 | 0.10.1 | >=0.11.2,<0.12 |
These dependencies are automatically resolved when using the Julia package manager.
We use a shared copyright model that enables all contributors to maintain the copyright on their contributions.
This software is licensed under the BSD-3-Clause license. See the LICENSE file for details.